Neonatal EEG classification using atomic decomposition

Belur Nagaraj, Sunil

Neonatal EEG classification using atomic decomposition

dc.check.embargoformat	E-thesis on CORA only	en
dc.check.entireThesis	Entire Thesis Restricted
dc.check.opt-out	No	en
dc.check.reason	This thesis is due for publication or the author is actively seeking to publish this material	en
dc.contributor.advisor	Lightbody, Gordon	en
dc.contributor.advisor	Marnane, William P.	en
dc.contributor.advisor	Boylan, Geraldine B.	en
dc.contributor.advisor	Stevenson, Nathan J.	en
dc.contributor.author	Belur Nagaraj, Sunil
dc.contributor.funder	Science Foundation Ireland	en
dc.date.accessioned	2015-12-02T10:26:32Z
dc.date.issued	2015
dc.date.submitted	2015
dc.description.abstract	The electroencephalogram (EEG) is an important noninvasive tool used in the neonatal intensive care unit (NICU) for the neurologic evaluation of the sick newborn infant. It provides an excellent assessment of at-risk newborns and formulates a prognosis for long-term neurologic outcome.The automated analysis of neonatal EEG data in the NICU can provide valuable information to the clinician facilitating medical intervention. The aim of this thesis is to develop a system for automatic classification of neonatal EEG which can be mainly divided into two parts: (1) classification of neonatal EEG seizure from nonseizure, and (2) classifying neonatal background EEG into several grades based on the severity of the injury using atomic decomposition. Atomic decomposition techniques use redundant time-frequency dictionaries for sparse signal representations or approximations. The first novel contribution of this thesis is the development of a novel time-frequency dictionary coherent with the neonatal EEG seizure states. This dictionary was able to track the time-varying nature of the EEG signal. It was shown that by using atomic decomposition and the proposed novel dictionary, the neonatal EEG transition from nonseizure to seizure states could be detected efficiently. The second novel contribution of this thesis is the development of a neonatal seizure detection algorithm using several time-frequency features from the proposed novel dictionary. It was shown that the time-frequency features obtained from the atoms in the novel dictionary improved the seizure detection accuracy when compared to that obtained from the raw EEG signal. With the assistance of a supervised multiclass SVM classifier and several timefrequency features, several methods to automatically grade EEG were explored. In summary, the novel techniques proposed in this thesis contribute to the application of advanced signal processing techniques for automatic assessment of neonatal EEG recordings.	en
dc.description.status	Not peer reviewed	en
dc.description.version	Accepted Version
dc.format.mimetype	application/pdf	en
dc.identifier.citation	Belur Nagaraj, S. 2015. Neonatal EEG classification using atomic decomposition. PhD Thesis, University College Cork.	en
dc.identifier.endpage	173
dc.identifier.uri	https://hdl.handle.net/10468/2117
dc.language.iso	en	en
dc.publisher	University College Cork	en
dc.relation.project	info:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2272/IE/Irish Centre for Fetal and Neonatal Translational Research (INFANT)/
dc.relation.project	info:eu-repo/grantAgreement/SFI/SFI Principal Investigator Programme (PI)/10/IN.1/B3036/IE/Pattern RecognitIon Systems for continuous neurological Monitoring in NEOnates [NEOPRISM]./
dc.rights	© 2015, Sunil Belur Nagaraj.	en
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/	en
dc.subject	Neonatal electroencephalogram	en
dc.subject	Atomic decomposition	en
dc.subject	Support vector machine	en
dc.thesis.opt-out	false
dc.title	Neonatal EEG classification using atomic decomposition	en
dc.type	Doctoral thesis	en
dc.type.qualificationlevel	Doctoral	en
dc.type.qualificationname	PHD (Engineering)	en
ucc.workflow.supervisor	g.lightbody@ucc.ie